Use of Graphic Images and Text Characters in Internet Banners as an Effective Marketing Tool

Participants were presented with a list of Internet banners and asked to perform a series of matching tasks to test the effectiveness of using graphic images as compared with verbal characters in web-site banners for e-marketing purposes. Participants studied ten different web pages. The web banners consisted of image or characters that had some degree of linkage to the corresponding web contents. Results show that: (a) participants have a greater ability to match web contents if banners are relevant to the web page contents; (b) contextual cues can be enriched by the context of the images used in the web banners; (c) frequent web surfers are less likely to recall web banners than normal web users. These findings are discussed in light of research on the effect of “picture superiority” and “semantic coding” on memory of pictures and words. This study concluded that the selection of graphic elements in web banners is important to an effective e-marketing strategy

Maintainability index for buildings

Thesis (B.Sc)--University of Hong Kong, 2008.Includes bibliographical references (p. 133-145).published_or_final_versio

Large Separable Kernel Attention: Rethinking the Large Kernel Attention Design in CNN

Visual Attention Networks (VAN) with Large Kernel Attention (LKA) modules have been shown to provide remarkable performance, that surpasses Vision Transformers (ViTs), on a range of vision-based tasks. However, the depth-wise convolutional layer in these LKA modules incurs a quadratic increase in the computational and memory footprints with increasing convolutional kernel size. To mitigate these problems and to enable the use of extremely large convolutional kernels in the attention modules of VAN, we propose a family of Large Separable Kernel Attention modules, termed LSKA. LSKA decomposes the 2D convolutional kernel of the depth-wise convolutional layer into cascaded horizontal and vertical 1-D kernels. In contrast to the standard LKA design, the proposed decomposition enables the direct use of the depth-wise convolutional layer with large kernels in the attention module, without requiring any extra blocks. We demonstrate that the proposed LSKA module in VAN can achieve comparable performance with the standard LKA module and incur lower computational complexity and memory footprints. We also find that the proposed LSKA design biases the VAN more toward the shape of the object than the texture with increasing kernel size. Additionally, we benchmark the robustness of the LKA and LSKA in VAN, ViTs, and the recent ConvNeXt on the five corrupted versions of the ImageNet dataset that are largely unexplored in the previous works. Our extensive experimental results show that the proposed LSKA module in VAN provides a significant reduction in computational complexity and memory footprints with increasing kernel size while outperforming ViTs, ConvNeXt, and providing similar performance compared to the LKA module in VAN on object recognition, object detection, semantic segmentation, and robustness tests

AudioInceptionNeXt: TCL AI LAB Submission to EPIC-SOUND Audio-Based-Interaction-Recognition Challenge 2023

This report presents the technical details of our submission to the 2023 Epic-Kitchen EPIC-SOUNDS Audio-Based Interaction Recognition Challenge. The task is to learn the mapping from audio samples to their corresponding action labels. To achieve this goal, we propose a simple yet effective single-stream CNN-based architecture called AudioInceptionNeXt that operates on the time-frequency log-mel-spectrogram of the audio samples. Motivated by the design of the InceptionNeXt, we propose parallel multi-scale depthwise separable convolutional kernels in the AudioInceptionNeXt block, which enable the model to learn the time and frequency information more effectively. The large-scale separable kernels capture the long duration of activities and the global frequency semantic information, while the small-scale separable kernels capture the short duration of activities and local details of frequency information. Our approach achieved 55.43% of top-1 accuracy on the challenge test set, ranked as 1st on the public leaderboard. Codes are available anonymously at https://github.com/StevenLauHKHK/AudioInceptionNeXt.git

Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations

Uncertainty estimation aims to evaluate the confidence of a trained deep neural network. However, existing uncertainty estimation approaches rely on low-dimensional distributional assumptions and thus suffer from the high dimensionality of latent features. Existing approaches tend to focus on uncertainty on discrete classification probabilities, which leads to poor generalizability to uncertainty estimation for other tasks. Moreover, most of the literature requires seeing the out-of-distribution (OOD) data in the training for better estimation of uncertainty, which limits the uncertainty estimation performance in practice because the OOD data are typically unseen. To overcome these limitations, we propose a new framework using data-adaptive high-dimensional hypothesis testing for uncertainty estimation, which leverages the statistical properties of the feature representations. Our method directly operates on latent representations and thus does not require retraining the feature encoder under a modified objective. The test statistic relaxes the feature distribution assumptions to high dimensionality, and it is more discriminative to uncertainties in the latent representations. We demonstrate that encoding features with Bayesian neural networks can enhance testing performance and lead to more accurate uncertainty estimation. We further introduce a family-wise testing procedure to determine the optimal threshold of OOD detection, which minimizes the false discovery rate (FDR). Extensive experiments validate the satisfactory performance of our framework on uncertainty estimation and task-specific prediction over a variety of competitors. The experiments on the OOD detection task also show satisfactory performance of our method when the OOD data are unseen in the training. Codes are available at https://github.com/HKU-MedAI/bnn_uncertainty.Comment: NeurIPS 202

China

Economic development processes in post-1949 China can be divided into two periods. In the first, 1950-70, the economy was extensively and intensively controlled by the state with a priority for developing heavy industries. In the second, since the 80s and known as the \u27reform period,\u27 the Chinese economy has increasingly been integrated with the world economy and relying on light (rural) industries as the prime motor of economic growth. Yet, in both these periods, Chinese policymakers shared the same \u27developmental\u27 philosophy in which social costs, that is the reproduction costs of human labour and nature, are largely ignored. The following is a critical sketch of government policies and their impact on the domestic population in these two periods

Cigarette Smoke-Induced Cerebral Cortical Interleukin-6 Elevation is not Mediated Through Oxidative Stress

The author group has previously established an in vivo subchronic cigarette smoke (CS) exposure rat model, in which the systemic oxidative burden as well as the modulation of local anti-oxidative enzymes in the lung has been demonstrated. Oxidative stress has been shown to induce pro-inflammatory cytokine release, including interleukin (IL)-6 in the airways. In this study, we aimed to investigate the changes in IL-6 production, as well as the oxidative/anti-oxidative responses in the cerebral cortex using the same in vivo model. IL-6 was determined by RT-PCR and western-blot analysis. Local oxidative and anti-oxidative responses were determined by measuring cerebral cortical malondialdehyde (MDA) and advanced oxidation protein product (AOPP) levels, superoxide dismutase (SOD) and catalase activities, and the reduced to oxidized glutathione (GSH/GSSG) ratio. Nitrite level was measured by fluorescent spectrophotometry. Our results demonstrated a significant increase in both IL-6 mRNA and protein levels. Reductions of SOD activity and manganese (Mn)SOD protein level were observed together with the increased level of superoxide measured by chemiluminescent signal, after 56 days of CS exposure. There were no significant changes in the cerebral cortical levels of MDA, AOPP, catalase activity, and the GSH/GSSG ratio. Nitrite level was significantly reduced, together with the decreased protein level of nNOS in the cerebral cortex, after 56 days of CS exposure. Our results suggest that exposure to CS induces IL-6 expression in the cerebral cortex, which is not mediated by the oxidative/anti-oxidative imbalance

Age composition and survival of public housing stock in Hong Kong

Emerging notably in more developed regions, building stock ageing which is characterised by shrinking new completions and falling “mortality” has been posing challenges to various stakeholders in built environment. To find way out of this transition, we need to know how long buildings will last these days and the factors leading to their “mortality”. By using data from 1950s till to date, a comprehensive investigation is conducted to analyse the age composition and life expectancy of public housing stock in Hong Kong. What comes after are survival analysis and empirical analysis of those demolished to identify the key factors leading to demolition. Presented in this paper are the preliminary findings as well as the research agenda on the theme to model age composition and survival of both private and public building stocks in Hong Kong and other similar cities in Asia Pacific Rim such as Adelaide and Singapore, together with research activities to formulate policies for sustainable urban management

Extracellular nanomatrix-induced self-organization of neural stem cells into miniature substantia nigra-like structures with therapeutic effects on Parkinsonian rats

Substantia nigra (SN) is a complex and critical region of the brain wherein Parkinson's disease (PD) arises from the degeneration of dopaminergic neurons. Miniature SN‐like structures (mini‐SNLSs) constructed from novel combination of nanomaterials and cell technologies exhibit promise as potentially curative cell therapies for PD. In this work, a rapid self‐organization of mini‐SNLS, with an organizational structure and neuronal identities similar to those of the SN in vivo, is achieved by differentiating neural stem cells in vitro on biocompatible silica nanozigzags (NZs) sculptured by glancing angle deposition, without traditional chemical growth factors. The differentiated neurons exhibit electrophysiological activity in vitro. Diverse physical cues and signaling pathways that are determined by the nanomatrices and lead to the self‐organization of the mini‐SNLSs are clarified and elucidated. In vivo, transplantation of the neurons from a mini‐SNLS results in an early and progressive amelioration of PD in rats. The sculptured medical device reported here enables the rapid and specific self‐organization of region‐specific and functional brain‐like structures without an undesirable prognosis. This development provides promising and significant insights into the screening of potentially curative drugs and cell therapies for PD

The photometric observation of the quasi-simultaneous mutual eclipse and occultation between Europa and Ganymede on 22 August 2021

Mutual events (MEs) are eclipses and occultations among planetary natural satellites. Most of the time, eclipses and occultations occur separately. However, the same satellite pair will exhibit an eclipse and an occultation quasi-simultaneously under particular orbital configurations. This kind of rare event is termed as a quasi-simultaneous mutual event (QSME). During the 2021 campaign of mutual events of jovian satellites, we observed a QSME between Europa and Ganymede. The present study aims to describe and study the event in detail. We observed the QSME with a CCD camera attached to a 300-mm telescope at the Hong Kong Space Museum Sai Kung iObservatory. We obtained the combined flux of Europa and Ganymede from aperture photometry. A geometric model was developed to explain the light curve observed. Our results are compared with theoretical predictions (O-C). We found that our simple geometric model can explain the QSME fairly accurately, and the QSME light curve is a superposition of the light curves of an eclipse and an occultation. Notably, the observed flux drops are within 2.6% of the theoretical predictions. The size of the event central time O-Cs ranges from -14.4 to 43.2 s. Both O-Cs of flux drop and timing are comparable to other studies adopting more complicated models. Given the event rarity, model simplicity and accuracy, we encourage more observations and analysis on QSMEs to improve Solar System ephemerides.Comment: 23 pages, 5 appendixes, 16 figures, 7 table